code: def test_h3_rnn_embed():
      # make repeatable
      set_random_seed(RANDOM_SEED)
  
      # setup encoder to test
      embed = h3_encoders.H3RNN().to(DEVICE)
      inputs = torch.tensor(
          [
              [
                  2,
                  0,
                  14,
                  102,
                  7,
                  0,
                  3,
                  5,
                  0,
                  5,
                  5,
                  0,
                  5,
                  7,
                  7,
                  7,
                  7,
                  7,
                  7,
              ],
              [
                  2,
                  0,
                  14,
                  102,
                  7,
                  0,
                  3,
                  5,
                  0,
                  5,
                  5,
                  0,
                  5,
                  7,
                  7,
                  7,
                  7,
                  7,
                  7,
              ],
          ],
          dtype=torch.int32,
      ).to(DEVICE)
      outputs = embed(inputs)
      assert outputs[ENCODER_OUTPUT].size()[1:] == embed.output_shape
  
      # check for parameter updating
      target = torch.randn(outputs[ENCODER_OUTPUT].shape)
      fpc, tpc, upc, not_updated = check_module_parameters_updated(
          embed, (inputs,), target
      )
      assert (
          tpc == upc
      ), f"Failed to update parameters. Parameters not updated: {not_updated}"
  
file_path: ludwig/tests/ludwig/encoders/test_h3_encoders.py
imports: import logging
  import torch
  from ludwig.constants import ENCODER_OUTPUT
  from ludwig.encoders import h3_encoders
  from ludwig.utils.misc_utils import set_random_seed
  from ludwig.utils.torch_utils import get_torch_device
  from tests.integration_tests.parameter_update_utils import check_module_parameters_updated
label_desc: Use image node_id 3 for calling a global function with example usage: test_h3_rnn_embed() without return types